Sparse Double-Geometric Nonlocal Mean image recovery via steerable kernel

It has been proved that the geometric information in images produce most stimulus of human eyes, which is important for the image recovery. However, the local geometric structure in images are too diverse to be accurately captured. Recent decade has witnessed a flourish of biological inspired algorithms in the image recovery. In this paper, inspired the adaptive and sparse characteristic of visual perception of humans, we advance an adaptive steerable kernel based Sparse Double-Geometric Nonlocal Mean (SDGNLM) denoising algorithm by exploring the local geometric information in both the “neighbor location” and “similarity measure”. In our method, a steerable kernel is employed to reveal the local geometric information of pixels, and sparse assumption of neighbors is cast on pixels to achieve more accurate image recovery. Moreover, a weighted sparse optimization algorithm is proposed to find and weight neighbors having the similar characteristics with each pixel. Some experiments are taken on some benchmark natural images, and the experimental results demonstrate its superiorities to NLM algorithm and its variants, in terms of both visual results and numerical guidelines.